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Does a Single ANN Properly Predict Pushover Response Parameters of Low-, Medium- and High-Rise Infilled RC Frames?

  • Research Article - Civil Engineering
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Abstract

Artificial neural networks, ANNs, can predict the behavior of systems that have common main features. When the problem under consideration contains groups that involve different main features, different ANNs are needed to predict the behavior of each group separately. In this paper, the efficiency of a single ANN to predict the lateral behavior of two-span structures representing a mix of low-, medium- and high-rise buildings in Egypt was investigated. All buildings were first analyzed using nonlinear pushover analysis to obtain their capacity curves, failure loads and displacements. Obtained data were used for training different ANN models. The results indicated the efficiency of a single ANN to predict the behavior of a mix of all buildings under investigation with a confidence level of 99%. The successful network was further utilized to obtain another set of data that were merged with the original data and used to develop a design neural network. The obtained network showed a very good capability to predict design variables which can be a good tool for engineering practitioners.

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Abbreviations

ANNs:

Artificial neural networks

FEM:

Finite element method

RC:

Reinforced concrete

MSE:

Mean square error

GFF:

Generalized feedforward network

GR:

General recurrent network

MP:

Multilayer perceptron network

PCA:

Principal component analysis network

M:

Momentum learning functions.

Q:

Quick-propagation learning functions.

CG:

Conjugate gradient learning functions.

\({t}_{\mathrm{w}}\) :

Thickness of masonry walls

\({E}_{\mathrm{s}}\) :

Steel modulus of elasticity

\({F}_{\mathrm{cu}}\) :

Compressive strength of concrete

\({F}_{\mathrm{tu}}\) :

Ultimate tensile strength of steel

\({\upupsilon }\) :

Poisson’s ratio

\({f}_{\mathrm{sy}}\) :

Yield strength of steel

(B.S/T.W):

Base shear over total weight

(T.D/T.H).:

Top displacement over total height

f(t):

Transfer function

SX:

The normalized value

\(R^{2}\) :

Coefficient of multiple determinations

L-ANN:

ANN for low-rise buildings

H-ANN:

ANN for high-rise buildings

M-ANN:

ANN for a mix of low- and high-rise buildings

D-ANN:

ANN for design of low- and high-rise buildings

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Authors and Affiliations

  1. Technical College, Alexandria, Egypt

    Khalid Abou El-Ftooh

  2. Tanta University, Tanta, Egypt

    Ayman A. Seleemah, Ahmed A. Atta & Salah El-Din F. Taher

Authors
  1. Khalid Abou El-Ftooh
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  2. Ayman A. Seleemah
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  3. Ahmed A. Atta
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  4. Salah El-Din F. Taher
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Correspondence to Ayman A. Seleemah.

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El-Ftooh, K.A., Seleemah, A.A., Atta, A.A. et al. Does a Single ANN Properly Predict Pushover Response Parameters of Low-, Medium- and High-Rise Infilled RC Frames?. Arab J Sci Eng 43, 5517–5539 (2018). https://doi.org/10.1007/s13369-018-3195-1

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